Video Annotation for Autonomous Vehicles
The potential of autonomous vehicles is huge. By supporting or replacing human drivers AI models can make roads safer and increase the efficiency of transport and delivery for us all. However, there still remain significant barriers to the widespread use of this technology. Autonomous vehicles will not be adopted until they are perceived to be almost entirely safe and result.
As a consequence computer vision AI companies are continuing to refine their autonomous vehicle systems with the help of video annotation. This process adds labels and segmentation to video frames and helps to train self-driving vehicles.
This blog will first look at the difficulties presented by video annotation in this industry. Secondly we will address the important applications that are supported by video annotation. And finally we will show how outsourcing to dedicated annotation services, like Keymakr, can help AI developers.
The unique challenge of video annotation
Video annotation is important for autonomous vehicles because video training data allows AI models to work in dynamically moving environments. However, video annotation can be difficult to do accurately and consistently. Even short videos contain thousands of frames that need to be annotated in the same way as a regular image. This makes video annotation more time consuming. It also requires more labour and management focus.
Despite the challenges of video annotation, this process is essential because of the use cases it enables:
- Object and vehicle detection: This crucial function allows autonomous vehicle AIs to identify obstacles and other vehicles and navigate around them.
- Environmental perception: Annotators use semantic segmentation techniques to create training data that labels every pixel in a video frame. This vital context allows AIs to understand their surroundings in more detail.
- Lane detection: Autonomous vehicles need to be able to recognise road lanes so that they can stay inside of them. Annotators support this capability by locating road markings in video frames.
- Understanding signage: Autonomous vehicles can automatically detect road signs and respond to them accordingly. Annotation services can enable this use case with careful video labeling.
Video annotation is also necessary for in-cabin AI applications:
- Monitoring behaviour and emotion: Driving whilst impaired can cause accidents. Therefore, automated driver monitoring systems are an increasing focus for AI developers. Facial feature annotation is Key to creating training data for this application.
- Driver’s assistant: When driver monitoring AI systems detect that a driver is falling asleep, for example, automated driver’s assistants can sound alerts that wake them up and advise them to rest.
- Object recognition: This capability is useful for in-cabin AIs because it can warn drivers and passengers when they have left valuable objects in their vehicle.
Key advantages from outsourcing
Video annotation can be a drain on the attention and resources for AI companies of all sizes. However, it is essential for the continued success of autonomous vehicles. Keymakr is an annotation service provider that offers important advantages to developers looking to outsource video annotation:
- Managed teams: Keymakr has an in-house team of annotators that are led by experienced managers. This structure ensures a higher degree of annotation quality in comparison with crowdsourced and remotely located annotations.
- Vector and bitmask: Keymakr provides both bitmasks and vector graphics for video annotation projects. In addition both image types can easily be converted from one to the other.
- Real time monitoring and alerts: Keymakr’s proprietary annotation platform boasts 24/7 monitoring capabilities. This system sends alerts when there are problems with data quality or delays in annotation projects, allowing managers to intervene early and correct ongoing issues.